{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# КОДИИМ Буткемп\n",
    "\n",
    "## Лекция 2\n",
    "\n",
    "### Polina Polunina & Boris Kovarsky\n",
    "polina.polunina@skolkovotech.ru\n",
    "\n",
    "# 1. Linear Regression and Classification Tutorial\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import packages:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#linear algebra\n",
    "import numpy as np\n",
    "#data structures\n",
    "import pandas as pd\n",
    "#ml models\n",
    "import scipy as sp\n",
    "import sklearn\n",
    "from sklearn import datasets\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.svm import SVR\n",
    "#plots\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "#beautiful plots\n",
    "import seaborn as sns\n",
    "#linear regression\n",
    "import statsmodels.api as sm\n",
    "#set style for plots\n",
    "sns.set_style('darkgrid')\n",
    "#off the warnings\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1 Simple Linear Regression\n",
    "Simple Linear Regression - Модель парной регрессии - регрессия с одной переменной"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Подгружаем датасет:\n",
    "* **SalePrice** - The property's sale price in dollars. Это целевая переменная (зависимая переменная), которую мы будем пытаться предсказать\n",
    "* **GrLivArea** - Above grade (ground) living area square feet - Это независимая переменная (предиктор)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('https://raw.githubusercontent.com/kondratevakate/machine-learning-with-love/master/02_lr/train.csv',\n",
    "                   index_col = 0, usecols=['Id', 'GrLivArea', 'SalePrice'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Давайте посмотрим на наши данные:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1710</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1262</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1786</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1717</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2198</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    GrLivArea  SalePrice\n",
       "Id                      \n",
       "1        1710     208500\n",
       "2        1262     181500\n",
       "3        1786     223500\n",
       "4        1717     140000\n",
       "5        2198     250000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1515.463699</td>\n",
       "      <td>180921.195890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>525.480383</td>\n",
       "      <td>79442.502883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>334.000000</td>\n",
       "      <td>34900.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1129.500000</td>\n",
       "      <td>129975.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1464.000000</td>\n",
       "      <td>163000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1776.750000</td>\n",
       "      <td>214000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5642.000000</td>\n",
       "      <td>755000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         GrLivArea      SalePrice\n",
       "count  1460.000000    1460.000000\n",
       "mean   1515.463699  180921.195890\n",
       "std     525.480383   79442.502883\n",
       "min     334.000000   34900.000000\n",
       "25%    1129.500000  129975.000000\n",
       "50%    1464.000000  163000.000000\n",
       "75%    1776.750000  214000.000000\n",
       "max    5642.000000  755000.000000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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K1p5pT3yeq9WOPbLunTEdeoTp0+dIrcxTbBt2ezbQB/Q3bo9cP6ahoRp9fZtaKGFimv0mIrXPVDzPE9XT071H1r0zpkOPUF6fzWZYK0fRPBoRCxq3TwTuBx4AToiIjog4COjIzHUtbFuS1Cat7MGfB1wfEXsBTwA3Z+ZQRNwPPEj9RWNRG2uUJLWgqYDPzGeAYxq3n6R+xMzIMcuAZe0rTZI0EZ7oJEmFMuAlqVAGvCQVyoCXpEIZ8JJUKANekgplwEtSoQx4SSqUAS9JhTLgJalQBrwkFcqAl6RCGfCSVCgDXpIKZcBLUqEMeEkqlAEvSYUy4CWpUAa8JBWqlS/dlnbKlq1D9PbObumxm18eZKB/c5srkqYHA16TbtaMKvMuur2lxz6zfCEDba5Hmi6copGkQhnwklQoA16SCmXAS1KhDHhJKlTLR9FExKPAhsbiz4HrgKuAQWB1Zv75xMuTJLWqpYCPiFkAmblg2LofAh8EfgbcHhFHZuYP2lGkJGnntboHfwTQHRGrG9tYBszMzKcBIuJO4D2AAS9JU6TVgN8EXAHcAPw6cAfQN+z+jcAh422kWq3Q09PdYgmaLqbqf6Ra7Sj+/3M69AjTp8+RWg34J4GnMrMGPBkRG4BfG3b/bF4b+KMaGqrR17epxRJa1+pp85oaU/E/AvUXlqn63bvKdOgRyuuz2Qxr9Sia04ErASLiLUA38FJEHBoRFeAE4P4Wty1JaoNW9+C/DKyMiH8EatQDfxvwt0CV+lE0329PiZKkVrQU8Jn5CvBHo9x1zMTKkSS1iyc6SVKhvFywdmteS15qnQGv3ZrXkpda5xSNJBXKgJekQhnwklQoA16SCmXAS1KhDHhJKpQBL0mFMuAlqVAGvCQVyoCXpEIZ8JJUKANekgplwEtSoQx4SSqUAS9JhfJ68CrWRL8sRNrTGfAq1kS/LGTr1qE2VyTtWk7RSFKhDHhJKpQBL0mFcg5eGsWWrUPMmlFt6UPazS8PMtC/eRKqknaOAS+NYqIf0A60uR6pFU7RSFKh2roHHxEdwBeAI4CXgY9m5lPt/B3b7TOni66ZvgHR7mcix9+DUzxqn3Yn5AeAWZn5HyPiGOBK4OQ2/w4AumZ2TugttDRZJjK9A/CTv/idCZ2gNRUvDhPd4fJFbXK0O+CPBb4DkJn/JyLe0ebtS8XbE+f/J7LDBdPvRW1X1Vyp1Wpt21hE3ACsysw7GsvPAYdk5o7O+14LPNu2AiRpejgY6B1vULv34PuB4S/DHWOEOzRRoCSpNe0+iuYB4H0AjTn4H7d5+5KkJrV7D/5W4Lcj4n8DFeAjbd6+JKlJbZ2DlyTtPjzRSZIKZcBLUqE8FXQnRMR84PLMXBARhwErgRrwGLAoM7dFxFJgITAILM7Mh3Y0dip6GEtEzABuBOYBM4HLgMcpqM+IqALXAwEMUf+cqEJBPW4XEfsDjwC/Tb2HlZTX46PAhsbiz4HrgKuo97M6M/98R2fYNw4Eec3YXd7AJHMPvkkRcQFwAzCrsWoFcGlm/hb1gDg5Io4EjgPmA38AfH5HY3dl7TvhVODFRp0nAtdQXp8nAWTmu4A/o15zaT1uf7G+Dth+Nk2JPc4CyMwFjZ+PANcCf0T9pMv5jR5fPcMeuIj6GfbsYGxRDPjmPQ2cMmz5KODexu07gPdS/0dZnZm1zHwO6IyI3h2M3R19A1gybHmQwvrMzG8CZzUWDwbWUFiPDVdQD7BfNZZL7PEIoDsiVkfEXRHxbmBmZj6dmTXgTuA9jDjDHnhHRMzZwdiiGPBNysxVwNZhqyqNfwyAjcC+wBz+7e3i8PWjjd3tZOZAZm6MiNnAzcCllNnnYER8Bbiaep9F9RgRHwbWZuadw1YX1WPDJuovZCcAHwNuaqzbbkd9DjXW9Y8ytigGfOuGz0nOBvp4/Zm829ePNna3FBFzgbuBv8nMr1Fon5n5J8C/oz4f3zXsrhJ6PJ36+Sj3AL8J/A9g/2H3l9AjwJPAVxvvQJ6kHuK/Nuz+HfXZMcq63bnPlhnwrXs0IhY0bp8I3E/9TN4TIqIjIg6ifqmGdTsYu9uJiDcBq4ELM/PGxuqi+oyI0yLi4sbiJuph9nBJPWbmuzPzuMxcAPwQ+BBwR0k9NpxOYz49It4CdAMvRcShEVGhvme/vc/XnGGfmf3AK6OMLYpH0bTuPOD6iNgLeAK4OTOHIuJ+4EHqL56LdjR2KgpuwiXAfsCSiNg+F38u8LmC+rwFuCki7gNmAIup11raczlSif+vXwZWRsQ/Uj/i53TqL9h/C1Spf77w/Yj4J0Y/w/5jI8fu6gYmm2eySlKhnKKRpEIZ8JJUKANekgplwEtSoQx4SSqUAS9JhTLgJalQBrwkFer/A8TET63gRiITAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e668c80e80>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.GrLivArea.hist(bins=20, label='GrLivArea')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e668f90a20>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.SalePrice.hist(bins=30, label='SalePrice')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "326099.99999999994"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.SalePrice.quantile(0.95)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e669066080>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#set figsize of the plot\n",
    "plt.figure(figsize = (10,7))\n",
    "#scatter plot of the data\n",
    "plt.scatter(data.GrLivArea, data.SalePrice)\n",
    "#text for x axis\n",
    "plt.xlabel('Living area, square feet')\n",
    "#text for y axis\n",
    "plt.ylabel('Sale Price, dollars')\n",
    "#text for the plot title\n",
    "plt.title('Dependence between House Sale Price and Living Area')\n",
    "#show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Как смоделировать эту зависимость?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Построение модели\n",
    "\n",
    "* Y = SalePrice - целевая, зависимая переменная\n",
    "* X = GrLivArea - предиктор, независимая переменная\n",
    "\n",
    "**Модель**\n",
    "\n",
    "Мы хотим найти прямую, которая наилучшим образом оторбражает зависимость между Sale Price и Living Area\n",
    "\n",
    "$Y = aX + b + \\epsilon$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Для начала построим модель ровно с одним параметром.\n",
    "\n",
    "Считая $b=0$, $Y=ax +\\epsilon$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.GrLivArea\n",
    "Y = data.SalePrice\n",
    "#define the model\n",
    "model = sm.OLS(Y, X)\n",
    "#fit the model\n",
    "results = model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>SalePrice</td>    <th>  R-squared (uncentered):</th>      <td>   0.919</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared (uncentered):</th> <td>   0.919</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>          <td>1.647e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 19 Feb 2024</td> <th>  Prob (F-statistic):</th>           <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>08:24:40</td>     <th>  Log-Likelihood:    </th>          <td> -18043.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  1460</td>      <th>  AIC:               </th>          <td>3.609e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  1459</td>      <th>  BIC:               </th>          <td>3.609e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>              <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>              <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>GrLivArea</th> <td>  118.0691</td> <td>    0.920</td> <td>  128.337</td> <td> 0.000</td> <td>  116.264</td> <td>  119.874</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>242.301</td> <th>  Durbin-Watson:     </th> <td>   2.024</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>4400.101</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.041</td>  <th>  Prob(JB):          </th> <td>    0.00</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td>11.504</td>  <th>  Cond. No.          </th> <td>    1.00</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] R² is computed without centering (uncentered) since the model does not contain a constant.<br/>[2] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}    &    SalePrice     & \\textbf{  R-squared (uncentered):}      &     0.919   \\\\\n",
       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared (uncentered):} &     0.919   \\\\\n",
       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       }          & 1.647e+04   \\\\\n",
       "\\textbf{Date:}             & Mon, 19 Feb 2024 & \\textbf{  Prob (F-statistic):}          &     0.00    \\\\\n",
       "\\textbf{Time:}             &     08:24:40     & \\textbf{  Log-Likelihood:    }          &   -18043.   \\\\\n",
       "\\textbf{No. Observations:} &        1460      & \\textbf{  AIC:               }          & 3.609e+04   \\\\\n",
       "\\textbf{Df Residuals:}     &        1459      & \\textbf{  BIC:               }          & 3.609e+04   \\\\\n",
       "\\textbf{Df Model:}         &           1      & \\textbf{                     }          &             \\\\\n",
       "\\textbf{Covariance Type:}  &    nonrobust     & \\textbf{                     }          &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                   & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{GrLivArea} &     118.0691  &        0.920     &   128.337  &         0.000        &      116.264    &      119.874     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       & 242.301 & \\textbf{  Durbin-Watson:     } &    2.024  \\\\\n",
       "\\textbf{Prob(Omnibus):} &   0.000 & \\textbf{  Jarque-Bera (JB):  } & 4400.101  \\\\\n",
       "\\textbf{Skew:}          &  -0.041 & \\textbf{  Prob(JB):          } &     0.00  \\\\\n",
       "\\textbf{Kurtosis:}      &  11.504 & \\textbf{  Cond. No.          } &     1.00  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] R² is computed without centering (uncentered) since the model does not contain a constant. \\newline\n",
       " [2] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                 OLS Regression Results                                \n",
       "=======================================================================================\n",
       "Dep. Variable:              SalePrice   R-squared (uncentered):                   0.919\n",
       "Model:                            OLS   Adj. R-squared (uncentered):              0.919\n",
       "Method:                 Least Squares   F-statistic:                          1.647e+04\n",
       "Date:                Mon, 19 Feb 2024   Prob (F-statistic):                        0.00\n",
       "Time:                        08:24:40   Log-Likelihood:                         -18043.\n",
       "No. Observations:                1460   AIC:                                  3.609e+04\n",
       "Df Residuals:                    1459   BIC:                                  3.609e+04\n",
       "Df Model:                           1                                                  \n",
       "Covariance Type:            nonrobust                                                  \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "GrLivArea    118.0691      0.920    128.337      0.000     116.264     119.874\n",
       "==============================================================================\n",
       "Omnibus:                      242.301   Durbin-Watson:                   2.024\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             4400.101\n",
       "Skew:                          -0.041   Prob(JB):                         0.00\n",
       "Kurtosis:                      11.504   Cond. No.                         1.00\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n",
       "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#plot the summary of our model\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GrLivArea    118.0691\n",
       "dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Согласно нашим рассчетам\n",
    "\n",
    "$$a=\\frac{\\sum xy}{\\sum x^2}$$\n",
    "\n",
    "Давайте проверим это:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#set size of the plot\n",
    "plt.figure(figsize = (10,7))\n",
    "#scatter plot of the data\n",
    "plt.scatter(data.GrLivArea, data.SalePrice, alpha=0.6, label = 'Original data')\n",
    "#plot of the found regression line\n",
    "plt.plot(data.GrLivArea.values, 118.0691 * data.GrLivArea.values, color = 'red', label='Model')\n",
    "#text for x axis\n",
    "plt.xlabel('Living area, square feet')\n",
    "#text for y axis\n",
    "plt.ylabel('Sale Price, dollars')\n",
    "#text for the plot title\n",
    "plt.title('Dependence between House Sale Price and Living Area')\n",
    "plt.legend()\n",
    "#show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Усложним модель, добавив константу"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.GrLivArea\n",
    "Y = data.SalePrice\n",
    "#define the model\n",
    "X = sm.add_constant(X)\n",
    "model = sm.OLS(Y, X)\n",
    "# X = sm.add_constant(X)\n",
    "#fit the model\n",
    "results = model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>SalePrice</td>    <th>  R-squared:         </th> <td>   0.502</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.502</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   1471.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 19 Feb 2024</td> <th>  Prob (F-statistic):</th> <td>4.52e-223</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>08:36:38</td>     <th>  Log-Likelihood:    </th> <td> -18035.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  1460</td>      <th>  AIC:               </th> <td>3.607e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  1458</td>      <th>  BIC:               </th> <td>3.608e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>     <td> 1.857e+04</td> <td> 4480.755</td> <td>    4.144</td> <td> 0.000</td> <td> 9779.612</td> <td> 2.74e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>GrLivArea</th> <td>  107.1304</td> <td>    2.794</td> <td>   38.348</td> <td> 0.000</td> <td>  101.650</td> <td>  112.610</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>261.166</td> <th>  Durbin-Watson:     </th> <td>   2.025</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>3432.287</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.410</td>  <th>  Prob(JB):          </th> <td>    0.00</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td>10.467</td>  <th>  Cond. No.          </th> <td>4.90e+03</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 4.9e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}    &    SalePrice     & \\textbf{  R-squared:         } &     0.502   \\\\\n",
       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &     0.502   \\\\\n",
       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &     1471.   \\\\\n",
       "\\textbf{Date:}             & Mon, 19 Feb 2024 & \\textbf{  Prob (F-statistic):} & 4.52e-223   \\\\\n",
       "\\textbf{Time:}             &     08:36:38     & \\textbf{  Log-Likelihood:    } &   -18035.   \\\\\n",
       "\\textbf{No. Observations:} &        1460      & \\textbf{  AIC:               } & 3.607e+04   \\\\\n",
       "\\textbf{Df Residuals:}     &        1458      & \\textbf{  BIC:               } & 3.608e+04   \\\\\n",
       "\\textbf{Df Model:}         &           1      & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}  &    nonrobust     & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                   & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{const}     &    1.857e+04  &     4480.755     &     4.144  &         0.000        &     9779.612    &     2.74e+04     \\\\\n",
       "\\textbf{GrLivArea} &     107.1304  &        2.794     &    38.348  &         0.000        &      101.650    &      112.610     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       & 261.166 & \\textbf{  Durbin-Watson:     } &    2.025  \\\\\n",
       "\\textbf{Prob(Omnibus):} &   0.000 & \\textbf{  Jarque-Bera (JB):  } & 3432.287  \\\\\n",
       "\\textbf{Skew:}          &   0.410 & \\textbf{  Prob(JB):          } &     0.00  \\\\\n",
       "\\textbf{Kurtosis:}      &  10.467 & \\textbf{  Cond. No.          } & 4.90e+03  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. \\newline\n",
       " [2] The condition number is large, 4.9e+03. This might indicate that there are \\newline\n",
       " strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:              SalePrice   R-squared:                       0.502\n",
       "Model:                            OLS   Adj. R-squared:                  0.502\n",
       "Method:                 Least Squares   F-statistic:                     1471.\n",
       "Date:                Mon, 19 Feb 2024   Prob (F-statistic):          4.52e-223\n",
       "Time:                        08:36:38   Log-Likelihood:                -18035.\n",
       "No. Observations:                1460   AIC:                         3.607e+04\n",
       "Df Residuals:                    1458   BIC:                         3.608e+04\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const       1.857e+04   4480.755      4.144      0.000    9779.612    2.74e+04\n",
       "GrLivArea    107.1304      2.794     38.348      0.000     101.650     112.610\n",
       "==============================================================================\n",
       "Omnibus:                      261.166   Durbin-Watson:                   2.025\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             3432.287\n",
       "Skew:                           0.410   Prob(JB):                         0.00\n",
       "Kurtosis:                      10.467   Cond. No.                     4.90e+03\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 4.9e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "const        18569.025856\n",
       "GrLivArea      107.130359\n",
       "dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Как хорошо найденная функция описывает реальную зависимость?\n",
    "**Y = 18569.0259 + 107.1304 * X**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#set size of the plot\n",
    "plt.figure(figsize = (10,7))\n",
    "#scatter plot of the data\n",
    "plt.scatter(data.GrLivArea, data.SalePrice, alpha=0.6, label = 'Original data')\n",
    "#plot of the found regression lines\n",
    "plt.plot(data.GrLivArea.values, 118.0691 * data.GrLivArea.values, color = 'red', label='Model')\n",
    "plt.plot(data.GrLivArea.values, 18569.0259 + 107.1304 * data.GrLivArea.values, color = 'green', label='Model')\n",
    "#text for x axis\n",
    "plt.xlabel('Living area, square feet')\n",
    "#text for y axis\n",
    "plt.ylabel('Sale Price, dollars')\n",
    "#text for the plot title\n",
    "plt.title('Dependence between House Sale Price and Living Area')\n",
    "plt.legend()\n",
    "#show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Оценка подгонки регрессии и $R^2$ \n",
    "\n",
    "Очередное напоминание из вашего курса статистики =)\n",
    "\n",
    "$R^2$ - это **коэффициент детерминации**, одна из наиболее популярных метрик для задачи регрессии, в том числе, для линейной регрессии\n",
    "\n",
    "В случае линейной регрессии, $R^2$ определяется следующим образом:\n",
    "\n",
    "* $y_i$ - наблюдаемые значения целевой переменной\n",
    "* $\\hat{y_i}$ - предсказываемые моделью значения целевой переменной\n",
    "* $\\overline{y} = \\frac{1}{n}\\sum_{i=1}^{n}y_i$ - среднее по наблюдаемым значениям целевой переменной\n",
    "* $SS_{tot} = \\sum_{i}(y_i - \\overline{y})^2$ - общая сумма квадратов (total sum of squares, TSS)\n",
    "* $SS_{reg} = \\sum_{i}(\\hat{y_i} - \\overline{y})^2$ - explained sum of squares, ESS\n",
    "* $SS_{res} = \\sum_{i}(y_i - \\hat{y_i})^2 = \\sum_{i}residual_i^2$ - residual sum of squares, RSS\n",
    "\n",
    "$R^2 = \\frac{SS_{reg}}{SS_{tot}} = 1 - \\frac{SS_{res}}{SS_{tot}}$ - доля объясненной дисперсии (ratio of the explained variance)\n",
    "\n",
    "$R^2$ лежит в интервале [0, 1]\n",
    "\n",
    "В нашей модели $R^2 = 0.502$, т.е. только половина от дисперсии объяснена "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** $R^2$ нестабилен, т.к. увеличивается вместе с добавлением новых данных, поэтому лучше смотреть на $R^2_{adjusted}$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Попробуем сделать лучше?\n",
    "\n",
    "Давайте возьмем логарифмы от X и Y. Тогда модель будет выглядеть так: **$ln(Y) = a*ln(X) + b + \\epsilon$**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.GrLivArea\n",
    "Y = data.SalePrice\n",
    "X=np.log(X)\n",
    "Y=np.log(Y)\n",
    "#add the constant term to the data\n",
    "X = sm.add_constant(X)\n",
    "#define the model\n",
    "model = sm.OLS(Y, X)\n",
    "#fit the model\n",
    "results = model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>SalePrice</td>    <th>  R-squared:         </th> <td>   0.502</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.502</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   1471.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 19 Feb 2024</td> <th>  Prob (F-statistic):</th> <td>4.52e-223</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>08:20:34</td>     <th>  Log-Likelihood:    </th> <td> -18035.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  1460</td>      <th>  AIC:               </th> <td>3.607e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  1458</td>      <th>  BIC:               </th> <td>3.608e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>     <td> 1.857e+04</td> <td> 4480.755</td> <td>    4.144</td> <td> 0.000</td> <td> 9779.612</td> <td> 2.74e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>GrLivArea</th> <td>  107.1304</td> <td>    2.794</td> <td>   38.348</td> <td> 0.000</td> <td>  101.650</td> <td>  112.610</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>261.166</td> <th>  Durbin-Watson:     </th> <td>   2.025</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>3432.287</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.410</td>  <th>  Prob(JB):          </th> <td>    0.00</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td>10.467</td>  <th>  Cond. No.          </th> <td>4.90e+03</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 4.9e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}    &    SalePrice     & \\textbf{  R-squared:         } &     0.502   \\\\\n",
       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &     0.502   \\\\\n",
       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &     1471.   \\\\\n",
       "\\textbf{Date:}             & Mon, 19 Feb 2024 & \\textbf{  Prob (F-statistic):} & 4.52e-223   \\\\\n",
       "\\textbf{Time:}             &     08:20:34     & \\textbf{  Log-Likelihood:    } &   -18035.   \\\\\n",
       "\\textbf{No. Observations:} &        1460      & \\textbf{  AIC:               } & 3.607e+04   \\\\\n",
       "\\textbf{Df Residuals:}     &        1458      & \\textbf{  BIC:               } & 3.608e+04   \\\\\n",
       "\\textbf{Df Model:}         &           1      & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}  &    nonrobust     & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                   & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{const}     &    1.857e+04  &     4480.755     &     4.144  &         0.000        &     9779.612    &     2.74e+04     \\\\\n",
       "\\textbf{GrLivArea} &     107.1304  &        2.794     &    38.348  &         0.000        &      101.650    &      112.610     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       & 261.166 & \\textbf{  Durbin-Watson:     } &    2.025  \\\\\n",
       "\\textbf{Prob(Omnibus):} &   0.000 & \\textbf{  Jarque-Bera (JB):  } & 3432.287  \\\\\n",
       "\\textbf{Skew:}          &   0.410 & \\textbf{  Prob(JB):          } &     0.00  \\\\\n",
       "\\textbf{Kurtosis:}      &  10.467 & \\textbf{  Cond. No.          } & 4.90e+03  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. \\newline\n",
       " [2] The condition number is large, 4.9e+03. This might indicate that there are \\newline\n",
       " strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:              SalePrice   R-squared:                       0.502\n",
       "Model:                            OLS   Adj. R-squared:                  0.502\n",
       "Method:                 Least Squares   F-statistic:                     1471.\n",
       "Date:                Mon, 19 Feb 2024   Prob (F-statistic):          4.52e-223\n",
       "Time:                        08:20:34   Log-Likelihood:                -18035.\n",
       "No. Observations:                1460   AIC:                         3.607e+04\n",
       "Df Residuals:                    1458   BIC:                         3.608e+04\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const       1.857e+04   4480.755      4.144      0.000    9779.612    2.74e+04\n",
       "GrLivArea    107.1304      2.794     38.348      0.000     101.650     112.610\n",
       "==============================================================================\n",
       "Omnibus:                      261.166   Durbin-Watson:                   2.025\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             3432.287\n",
       "Skew:                           0.410   Prob(JB):                         0.00\n",
       "Kurtosis:                      10.467   Cond. No.                     4.90e+03\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 4.9e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#plot the summary of our model\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#set size of the plot\n",
    "plt.figure(figsize = (10,7))\n",
    "#scatter plot of the data\n",
    "plt.scatter(np.log(data.GrLivArea), np.log(data.SalePrice), alpha=0.6, label = 'Original data')\n",
    "#plot of the found regression line\n",
    "plt.plot(np.log(data.GrLivArea.values), 5.6681 + 0.8745 * np.log(data.GrLivArea.values), color = 'orchid', label='Model')\n",
    "#text for x axis\n",
    "plt.xlabel('log(Living area), square feet')\n",
    "#text for y axis\n",
    "plt.ylabel('log(Sale Price), dollars')\n",
    "#text for the plot title\n",
    "plt.title('Dependence between log(House Sale Price) and log(Living Area)')\n",
    "plt.legend()\n",
    "#show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2 Multiple Linear Regression (Модель множественной линейной регрессии)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols=['Id', 'MSSubClass', 'LotArea', 'OverallQual',\\\n",
    "      'OverallCond', 'YearBuilt', 'YearRemodAdd',\\\n",
    "      'BsmtUnfSF', 'TotalBsmtSF', '1stFlrSF', '2ndFlrSF',\\\n",
    "     'LowQualFinSF', 'GrLivArea', 'BsmtFullBath',\\\n",
    "     'FullBath', 'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr',\\\n",
    "     'TotRmsAbvGrd', 'Fireplaces', 'GarageCars',\\\n",
    "     'GarageArea', 'WoodDeckSF', 'OpenPorchSF', 'EnclosedPorch', '3SsnPorch',\\\n",
    "     'ScreenPorch', 'PoolArea', 'MiscVal', 'MoSold', 'YrSold', 'SalePrice']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <th>...</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>60</td>\n",
       "      <td>8450</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2003</td>\n",
       "      <td>2003</td>\n",
       "      <td>150</td>\n",
       "      <td>856</td>\n",
       "      <td>856</td>\n",
       "      <td>854</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>9600</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>1976</td>\n",
       "      <td>1976</td>\n",
       "      <td>284</td>\n",
       "      <td>1262</td>\n",
       "      <td>1262</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>298</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60</td>\n",
       "      <td>11250</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2001</td>\n",
       "      <td>2002</td>\n",
       "      <td>434</td>\n",
       "      <td>920</td>\n",
       "      <td>920</td>\n",
       "      <td>866</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>70</td>\n",
       "      <td>9550</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1915</td>\n",
       "      <td>1970</td>\n",
       "      <td>540</td>\n",
       "      <td>756</td>\n",
       "      <td>961</td>\n",
       "      <td>756</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>272</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>60</td>\n",
       "      <td>14260</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>490</td>\n",
       "      <td>1145</td>\n",
       "      <td>1145</td>\n",
       "      <td>1053</td>\n",
       "      <td>...</td>\n",
       "      <td>192</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    MSSubClass  LotArea  OverallQual  OverallCond  YearBuilt  YearRemodAdd  \\\n",
       "Id                                                                           \n",
       "1           60     8450            7            5       2003          2003   \n",
       "2           20     9600            6            8       1976          1976   \n",
       "3           60    11250            7            5       2001          2002   \n",
       "4           70     9550            7            5       1915          1970   \n",
       "5           60    14260            8            5       2000          2000   \n",
       "\n",
       "    BsmtUnfSF  TotalBsmtSF  1stFlrSF  2ndFlrSF  ...  WoodDeckSF  OpenPorchSF  \\\n",
       "Id                                              ...                            \n",
       "1         150          856       856       854  ...           0           61   \n",
       "2         284         1262      1262         0  ...         298            0   \n",
       "3         434          920       920       866  ...           0           42   \n",
       "4         540          756       961       756  ...           0           35   \n",
       "5         490         1145      1145      1053  ...         192           84   \n",
       "\n",
       "    EnclosedPorch  3SsnPorch  ScreenPorch  PoolArea  MiscVal  MoSold  YrSold  \\\n",
       "Id                                                                             \n",
       "1               0          0            0         0        0       2    2008   \n",
       "2               0          0            0         0        0       5    2007   \n",
       "3               0          0            0         0        0       9    2008   \n",
       "4             272          0            0         0        0       2    2006   \n",
       "5               0          0            0         0        0      12    2008   \n",
       "\n",
       "    SalePrice  \n",
       "Id             \n",
       "1      208500  \n",
       "2      181500  \n",
       "3      223500  \n",
       "4      140000  \n",
       "5      250000  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('train.csv', index_col=0, usecols=cols)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проверка на пропуски в данных (nan):"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Определяем модель"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.drop('SalePrice', axis=1)\n",
    "Y = data.SalePrice\n",
    "#add the constant term to the data\n",
    "X = sm.add_constant(X)\n",
    "#define the model\n",
    "model = sm.OLS(Y, X)\n",
    "#fit the model\n",
    "results = model.fit()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Вычисляем некоторые статистические параметры:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " the number of paraters to estimate: 31 \n",
      " total number of observations: 1460 \n",
      " degrees of freedom of the model: 30 \n",
      " degrees of freedom of the errors: 1429\n"
     ]
    }
   ],
   "source": [
    "#together with the intercept\n",
    "k = X.shape[1]\n",
    "#total number of observations\n",
    "n = X.shape[0]\n",
    "#degrees of freedom for the model:\n",
    "df_model = k - 1\n",
    "#degrees of freedom of the error:\n",
    "df_error = n - k\n",
    "print(' the number of paraters to estimate: {} \\n total number of observations: {} \\n degrees of freedom of the model: {} \\n degrees of freedom of the errors: {}'\\\n",
    "      .format(k, n, df_model, df_error))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Посчитаем ранг матрицы наблюдений Х:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Logistic Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Подгружаем датасет:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length</th>\n",
       "      <th>sepal width</th>\n",
       "      <th>petal length</th>\n",
       "      <th>petal width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length  sepal width  petal length  petal width\n",
       "0           5.1          3.5           1.4          0.2\n",
       "1           4.9          3.0           1.4          0.2\n",
       "2           4.7          3.2           1.3          0.2\n",
       "3           4.6          3.1           1.5          0.2\n",
       "4           5.0          3.6           1.4          0.2"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X, Y = sklearn.datasets.load_iris(return_X_y=True)\n",
    "names = ['sepal length', 'sepal width', 'petal length', 'petal width']\n",
    "classes = ['setosa', 'versicolor', 'virginica']\n",
    "#create pandas object\n",
    "X = pd.DataFrame(X, columns=names)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Целевая переменная уже закодирована:\n",
    "    * 0 - Setosa\n",
    "    * 1 - Versicolor\n",
    "    * 2 - Verginica\n",
    "    \n",
    "<img style=\"float: left;\" src=\"iris.jpg\" br>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Описательные статистики:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length</th>\n",
       "      <th>sepal width</th>\n",
       "      <th>petal length</th>\n",
       "      <th>petal width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.057333</td>\n",
       "      <td>3.758000</td>\n",
       "      <td>1.199333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.435866</td>\n",
       "      <td>1.765298</td>\n",
       "      <td>0.762238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sepal length  sepal width  petal length  petal width\n",
       "count    150.000000   150.000000    150.000000   150.000000\n",
       "mean       5.843333     3.057333      3.758000     1.199333\n",
       "std        0.828066     0.435866      1.765298     0.762238\n",
       "min        4.300000     2.000000      1.000000     0.100000\n",
       "25%        5.100000     2.800000      1.600000     0.300000\n",
       "50%        5.800000     3.000000      4.350000     1.300000\n",
       "75%        6.400000     3.300000      5.100000     1.800000\n",
       "max        7.900000     4.400000      6.900000     2.500000"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21580054e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21580091160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x215fcc5a748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21580005588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for name in names:\n",
    "    X[name].hist(bins=9, label=name)\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Корреляция:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e6697ff3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt=pd.concat([X,pd.DataFrame(Y, columns=['target'])], axis=1)\n",
    "sns.set(style=\"white\")\n",
    "\n",
    "# Compute the correlation matrix\n",
    "corr = dt.corr()\n",
    "\n",
    "# Generate a mask for the upper triangle\n",
    "mask = np.zeros_like(corr, dtype=np.bool)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "\n",
    "# Set up the matplotlib figure\n",
    "f, ax = plt.subplots(figsize=(11, 9))\n",
    "\n",
    "# Generate a custom diverging colormap\n",
    "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
    "\n",
    "# Draw the heatmap with the mask and correct aspect ratio\n",
    "\n",
    "sns.heatmap(corr, mask=mask, cmap=cmap, center=0,\n",
    "            square=True, linewidths=.5, cbar_kws={\"shrink\": .5})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Еще несколько красивых графиков:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e668f7bb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(dt, kind='scatter', hue='target')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x215fd2f7a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot( x=dt[\"target\"], y=dt[\"sepal length\"])\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Считаем ранг матрицы признаков:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linalg.matrix_rank(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: Maximum number of iterations has been exceeded.\n",
      "         Current function value: 0.072266\n",
      "         Iterations: 35\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9666666666666667"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#from sklearn import metrics \n",
    "logit = sm.MNLogit(Y, X)\n",
    "result = logit.fit()\n",
    "preds=np.argmax(result.predict(X).values,axis=1)\n",
    "accuracy_score(Y,preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Logistic regression with L1 regularization**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.98"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "model=sklearn.linear_model.LogisticRegression(penalty='l1',multi_class='multinomial', solver='saga').fit(X,Y)\n",
    "preds=model.predict(X)\n",
    "accuracy_score(Y, preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Logistic regression with L2 regularization**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9866666666666667"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "model=sklearn.linear_model.LogisticRegression(penalty='l2',multi_class='multinomial', solver='saga', fit_intercept=True).fit(X,Y)\n",
    "preds=model.predict(X)\n",
    "accuracy_score(Y, preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "newton-cg 0.9733333333333334\n",
      "sag 0.9866666666666667\n",
      "saga 0.9866666666666667\n",
      "lbfgs 0.9733333333333334\n"
     ]
    }
   ],
   "source": [
    "solvers=['newton-cg', 'sag', 'saga', 'lbfgs']\n",
    "for solver in solvers:\n",
    "    model=sklearn.linear_model.LogisticRegression(penalty='l2',multi_class='multinomial', solver=solver, fit_intercept=True).fit(X,Y)\n",
    "    preds=model.predict(X)\n",
    "    print(solver, accuracy_score(Y, preds))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Анализируем результаты"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Визуальный анализ**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 3: \n",
    "* постройте точечные диаграммы (scatter plots) любых двух признаков из X\n",
    "    * a) используйте настоящие значения целевой переменной в качестве цветовой разметки\n",
    "    * b) используйте предсказанные значения в качестве цветовой разметки\n",
    "    \n",
    "**Hint:** используйте sns.lmplot  \n",
    "\n",
    "* найдите как минимум две неправильно классифицированные точки"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Confusion matrix**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 4:\n",
    "* прочитайте про confusion matrix (https://en.wikipedia.org/wiki/Confusion_matrix)\n",
    "* посчитайте и нарисуйте ее (https://scikit-learn.org/0.20/auto_examples/model_selection/plot_confusion_matrix.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 5: Постройте (любую) модель логистической регрессии для двух классов: versicolor и virginica\n",
    "* 0) постройте график парных зависимостей и сделайте цветовую разметку по значениям целевой переменной\n",
    "* a) сформируйте новый датасет, соответствующий поставленной задаче (удалите все, что относится к классу setosa)\n",
    "* b) постройте модель\n",
    "* c) выведите на экран accuracy score"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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